Automation
The systems that build and run the platform.
Automation is how compute, network and storage stay repeatable after the design is agreed. Dynacore writes the definitions, the pipelines and the checks that bring a platform up and keep it that way.
What we mean by automation
An infrastructure practice is unfinished if the only copy of the platform is the one that happens to be running. Automation is the record that can rebuild it: code for the infrastructure, configuration that is applied the same way each time, and checks that say whether the result matches the design.
The work covers compute, network and storage, the pipelines that promote a change, the bring up of an AI factory or GPU cluster, and the workflows around that platform. Where a step needs judgement rather than a rule, it can call an agent. That boundary is designed with the Agentic AI practice, not left as an open tool.
Infrastructure as code and configuration
Networks, storage, identity bindings and the host build are written down, reviewed and applied from that record. A change is a review. It is not a console session that nobody can repeat next quarter.
Configuration automation keeps the running system aligned with that record. Drift is something you can see, and something you can decide to correct, rather than a surprise during an incident.
Provisioning and day 2 operations
Automated provisioning covers the first build of compute, network and storage. Day 2 is the work after that system is in use: capacity added the same way, a failed node replaced from the same definition, and a routine for patching that has an owner.
We write the operating notes with the people who will run the platform. A note that only the author understands is not a handover.
CI/CD and GitOps for platforms
Platform pipelines promote a change through environments with a record of who approved it. GitOps keeps the declared state in a repository and applies that state to the platform. We design the promotion path, how secrets are held, and how you go back if a change is wrong.
The tool follows the platform you already run. We do not publish a catalogue as if one product were mandatory. The Cloud and workplace practice is where the landing zone and the identity for those pipelines are designed.
AI factory and GPU cluster bring up
Bringing up an AI factory or a GPU cluster is the same idea applied to accelerated compute. Validation automation checks power, thermals, fabric, storage and a known failure case before the cluster is called ready.
The design of the factory is the AI Factory practice. This practice is the repeatable way that design is stood up, checked and handed over. We do not treat a successful first boot as commissioning.
Workflow and process automation
Around the platform are the steps people already take: a request that becomes a change, an approval that is recorded, a job that runs because a rule says it should. Those steps stay deterministic when the rule is clear.
An agent driven workflow is used where the input is messy and the cost of a checked mistake is acceptable. The agent has an owner, a limit on its tools, and a way for a person to stop it. That design is Agentic AI. The path into business systems is AI integration.
Observability and automated remediation
Observability is part of the operating model: signals for capacity, failure and drift, and a response that is written down. Automated remediation is used where the action is safe to repeat, such as restarting a known failed task or opening a ticket with the evidence attached.
An action that changes access, money, or a record about a person still stops for a person. We list which remediations are automatic and which wait. We do not describe a security operations centre by describing the signals.
Answers
Direct answers
Short statements for the questions this page is here to settle.
What does the automation practice cover?
Infrastructure as code, configuration, provisioning and day 2 operations for compute, network and storage, platform pipelines, validation for AI factory and GPU clusters, workflow automation, and observability with remediation that is safe to repeat.
How does automation relate to agentic AI?
A workflow can call an agent where the step needs judgement. The agent still needs an owner, a limit on its tools, and a way for a person to stop it. That design is the Agentic AI practice. Automation is the repeatable path around it.
Brief us on the platform you need to repeat
Describe what is built by hand today, and what a failed change would cost.